课程信息
A practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction.
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Clock

Approx. 22 hours to complete

建议:8 weeks of study, 2-3 hours/week
Comment Dots

English

字幕:English
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Clock

Approx. 22 hours to complete

建议:8 weeks of study, 2-3 hours/week
Comment Dots

English

字幕:English

Syllabus - What you will learn from this course

1

Section
Clock
4 hours to complete

Introduction and Module 1A: Simple Regression Methods

In this module, a unified structure for simple regression models will be presented, followed by detailed treatises and examples of both simple linear and logistic models....
Reading
11 videos (Total 203 min), 3 readings
Video11 videos
Lecture 1a: Simple Regression: An Overview17m
Lecture 1b: Simple Linear Regression with a Binary (or Nominal Categorical) Predictor 21m
Lecture 1c: Simple Linear Regression with a Continuous Predictor 30m
Lecture 1d: Simple Linear Regression Model: Estimating the Regression Equation—Accounting for Uncertainty in the Estimates 22m
Lecture 1e: Measuring the Strength of a Linear Association 25m
Lecture 2 Introduction: Simple Logistic Regression1m
Lecture 2a: Simple Logistic Regression with a Binary (or Categorical) Predictor 24m
Lecture 2b: Simple Logistic Regression with a Continuous Predictor 24m
Lecture 2c: Simple Logistic Regression: Accounting for Uncertainty in the Estimates 19m
Lecture 2d: Estimating Risk and Functions of Risk from Logistic Regression Results 14m
Reading3 readings
Syllabus10m
Learning Objectives, Lecture 110m
Learning Objectives, Lecture 210m

2

Section
Clock
4 hours to complete

Module 1B: More Simple Regression Methods

In this model, more detail is given regarding Cox regression, and it's similarities and differences from the other two regression models from module 1A. The basic structure of the model is detailed, as well as its assumptions, and multiple examples are presented....
Reading
5 videos (Total 74 min), 3 readings, 8 quizzes
Video5 videos
Lecture 3a: Simple Cox Regression: The Concept of Proportional Hazards 19m
Lecture 3b: Simple Cox Regression with Binary or Categorical Predictors 11m
Lecture 3d: Accounting for Uncertainty in Slope Estimate and Translating Cox Regression Results to Predicted Survival Curves 19m
Lecture 3c: Simple Cox Regression with a Continuous Predictor 21m
Reading3 readings
Learning Objectives, Lecture 310m
Supporting Information for Homework 110m
Quiz 1 Solutions10m
Quiz8 practice exercises
Homework 1A16m
Homework 1B22m
Homework 1C10m
Homework 1D10m
Homework 1E10m
Homework 1F10m
Homework 1G14m
Module 1 Quiz: Covers Lectures 1-324m

3

Section
Clock
1 hour to complete

Module 2A: Confounding and Effect Modification (Interaction)

This module, along with module 2B introduces two key concepts in statistics/epidemiology, confounding and effect modification. A relation between an outcome and exposure of interested can be confounded if a another variable (or variables) is associated with both the outcome and the exposure. In such cases the crude outcome/exposure associate may over or under-estimate the association of interest. Confounding is an ever-present threat in non-randomized studies, but results of interest can be adjusted for potential confounders. ...
Reading
4 videos (Total 54 min), 1 reading
Video4 videos
Lecture 4a: Confounding: A Formal Definition and Some Examples 24m
Lecture 4b: Adjusted Estimates: Presentation, Interpretation, and Utility for Assessing Confounding 17m
Lecture 4c: Adjusted Estimates: The General Idea Behind the Computations 10m
Reading1 readings
Learning Objectives, Lecture 410m

4

Section
Clock
3 hours to complete

Module 2B: Effect Modification (Interaction

Effect modification (Interaction), unlike confounding, is a phenomenon of "nature" and cannot be controlled by study design choice. However, it can be investigated in a manner similar to that of confounding. This set of lectures will define and give examples of effect modification, and compare and contrast it with confounding....
Reading
4 videos (Total 65 min), 3 readings, 5 quizzes
Video4 videos
Lecture 5a: Effect Modification: Introduction with Some Examples 28m
Lecture 5b: Effect Modification: More Examples of Investigating Effect Modification 19m
Lecture 5c: Confounding versus Effect Modification: A Review 15m
Reading3 readings
Learning Objectives, Lecture 510m
Supporting Information for Homework 210m
Quiz 2 Solutions10m
Quiz5 practice exercises
Homework 2A22m
Homework 2B6m
Homework 2C4m
Homework 2D8m
Module 2 Quiz: Covers Lectures 1-524m

5

Section
Clock
3 hours to complete

Module 3A: Multiple Regression Methods

This module extends linear and logistic methods to allow for the inclusion of multiple predictors in a single regression model....
Reading
8 videos (Total 172 min), 2 readings
Video8 videos
Lecture 6b: Multiple Linear Regression: Some Examples 25m
Lecture 6c: Multiple Linear Regression: Basics of Model Selection and Estimating Outcomes 19m
Lecture 6d: Multiple Linear Regression: Some Examples from the Literature 23m
Lecture 7 Introduction: Multiple Logistic Regression1m
Lecture 7a: Multiple Logistic Regression: Some Examples 30m
Lecture 7b: Basics of Model Selection and Estimating Outcomes 22m
Lecture 7c: Some Examples from the Literature 33m
Reading2 readings
Learning Objectives, Lecture 610m
Learning Objectives, Lecture 710m

6

Section
Clock
5 hours to complete

Module 3B: More Multiple Regression Methods

This set of lectures extends the techniques debuted in lecture set 3 to allow for multiple predictors of a time-to-event outcome using a single, multivariable regression model....
Reading
8 videos (Total 160 min), 4 readings, 5 quizzes
Video8 videos
Lecture 8a: Multiple Cox PH Regression: Some Examples 18m
Lecture 8b: Multiple Cox Regression: Basics of Model Selection and Estimating Outcomes 22m
Lecture 8c: Multiple Cox Regression: Some Examples from the Literature 28m
Lecture 9 Introduction: Investigating Effect Modification and Non-Linear Relationships with Multiple Regression2m
Lecture 9a: Effect Modification and Non-Linear Associations: Regression Based Approaches 30m
Lecture 9b: Examples of Interaction Terms from Published Research 26m
Lecture 9c: Non-Linear Relationships with Continuous Predictors in Regression: The Spline Approach 28m
Reading4 readings
Learning Objectives, Lecture 810m
Learning Objectives, Lecture 910m
Supporting Information for Homework 310m
Quiz 3 Solutions10m
Quiz5 practice exercises
Homework 3A12m
Homework 3B14m
Homework 3C14m
Homework 3D14m
Module 3 Quiz: Covers Lectures 1-824m

7

Section
Clock
3 hours to complete

Module 4: Additional Topics in Regression

...
Reading
4 videos (Total 73 min), 3 readings, 4 quizzes
Video4 videos
Lecture 10a: Propensity Scores: Definition and Adjustment 26m
Lecture 10b: More Examples of Propensity Score Adjustment 18m
Lecture 10c: Propensity Score Matching 23m
Reading3 readings
Supporting Information for Homework 410m
Quiz 4 Solutions10m
Learning Objectives, Lecture 1010m
Quiz4 practice exercises
Homework 4A12m
Homework 4B8m
Homework 4C6m
Module 4 Quiz: Covers Lectures 1-1038m
4.7
Briefcase

83%

got a tangible career benefit from this course
Money

25%

got a pay increase or promotion

Top Reviews

By MJJun 8th 2017

Very well taught course. I learned valuable skills, and got a better understanding of how to interpret results, published in the literature.

By XPJan 8th 2017

Great course to improve your skills related to statistical data analysis focused on health domain

Instructor

Avatar

John McGready, PhD, MS

Associate Scientist, Biostatistics

About Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world....

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